Neural networks are built with layers connected to each other. There are many different kind of layers. For image related applications, you can always find convolutional layers. It is a layer with very few parameters but applied over a large sized input. It is powerful because it can preserve the spatial structure of the image. […]
What neural network is appropriate for your predictive modeling problem? It can be difficult for a beginner to the field of deep learning to know what type of network to use. There are so many types of networks to choose from and new methods being published and discussed every day. To make things worse, most […]
Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed, and they are neither magical nor hard to understand. In this tutorial, you will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How […]
By Vincent Granville, Ph.D., Author at MLtechniques.com Sponsored Post Very deep neural networks (VDNN) illustrated with data animation: a 40 second video, featuring supervised learning, layers, neurons, fuzzy classification, and convolution filters. It is said that a picture is worth a thousand words. Here instead, I use a video to illustrate the concept of very deep […]
An artificial neural network is a computational model that approximates a mapping between inputs and outputs. It is inspired by the structure of the human brain, in that it is similarly composed of a network of interconnected neurons that propagate information upon receiving sets of stimuli from neighbouring neurons. Training a neural network involves a […]
Weight initialization is an important design choice when developing deep learning neural network models. Historically, weight initialization involved using small random numbers, although over the last decade, more specific heuristics have been developed that use information, such as the type of activation function that is being used and the number of inputs to the node. […]
Deep learning neural networks are a flexible class of machine learning algorithms that perform well on a wide range of problems. Neural networks are trained using the backpropagation of error algorithm that involves calculating errors made by the model on the training dataset and updating the model weights in proportion to those errors. The limitation […]
Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models. Unlike other deep learning neural network models that are trained with a loss function until convergence, a GAN generator model is trained using a second model called a discriminator that learns to classify images as real or generated. […]
Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled “Generative Adversarial Networks.” Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images. As such, a number of books […]
The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. It is an important extension to the GAN model and requires a conceptual shift away from a […]
